Abstract
Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.
Highlights
As the global urbanization process generally accelerates, urban landscape patterns are changing more and more frequently, which has an important impact on the life, environment, and development of different regions of the world [1]
On the basis of summarizing and analyzing the existing intra-urban surface water (IUSW) literatures [10,32,33,41,42], this paper suggests that there are still three issues that need to be further addressed, and the comprehensive solution through the three issues is reflected in the contribution of this paper: 1. A technique is proposed to fuse the abundant water indices in the spectral dimension into high-resolution remote sensing images in order to solve the commission problem caused by insufficient spectral information to extract IUSW from high spatial resolution images
We proposed the MLWI/MSWI to describe the spectral–spatial properties of water using an extended morphological profile method based on the normalized difference water index (NDWI), which was characterized by a combination of the water body index and morphological information, thereby improving the recognition accuracy of different forms of water bodies in urban scenes
Summary
As the global urbanization process generally accelerates, urban landscape patterns are changing more and more frequently, which has an important impact on the life, environment, and development of different regions of the world [1]. The rapid and accurate acquisition of intra-urban surface water (IUSW) area information is crucial for the sustainable urban development. With the development and application of remote sensing satellites, remote sensing technology has become an important way to monitor urban surface water [8]. A variety of remote sensing sensors were applied for water surface area extraction, such as synthetic aperture radar [9], LIDAR [10], and medium- and high-spatial-resolution optical satellite images [11,12]. High-resolution remote sensing images have become the main data source for IUSW monitoring [14,15,16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
